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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.9212222639305973
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.9094896980313647
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.8903518101434769
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.8988457207207208
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.9226903987320654
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.9193641558224892
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Random Forest Accuracy = 0.9156792208875543
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Original Image Gabor1 Gabor2 ... Median s3 Variance s3 Labels
0 0 0 0 ... 0 0 0
1 0 0 0 ... 0 0 0
2 0 0 0 ... 0 0 0
3 0 0 0 ... 0 0 0
4 0 0 0 ... 0 0 0
[5 rows x 44 columns]
Traceback (most recent call last):
File C:\Dev\Python\biomedical_term\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec
exec(code, globals, locals)
File c:\dev\python\biomedical_term\homew.py:453
result_selected_perm=model_perm.predict(X_perm)
File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\ensemble\_forest.py:820 in predict
proba = self.predict_proba(X)
File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\ensemble\_forest.py:873 in predict_proba
Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
File C:\Dev\Python\biomedical_term\Lib\site-packages\sklearn\utils\parallel.py:63 in __call__
return super().__call__(iterable_with_config)
File C:\Dev\Python\biomedical_term\Lib\site-packages\joblib\parallel.py:1098 in __call__
self.retrieve()
File C:\Dev\Python\biomedical_term\Lib\site-packages\joblib\parallel.py:975 in retrieve
self._output.extend(job.get(timeout=self.timeout))
File ~\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\pool.py:768 in get
self.wait(timeout)
File ~\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\pool.py:765 in wait
self._event.wait(timeout)
File ~\AppData\Local\Programs\Python\Python311\Lib\threading.py:622 in wait
signaled = self._cond.wait(timeout)
File ~\AppData\Local\Programs\Python\Python311\Lib\threading.py:320 in wait
waiter.acquire()
KeyboardInterrupt
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In [9]: